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An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems

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  • Antonio Del Corte-Valiente

    (Department of Computer Engineering, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain)

  • José Luis Castillo-Sequera

    (Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain)

  • Ana Castillo-Martinez

    (Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain)

  • José Manuel Gómez-Pulido

    (Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain)

  • Jose-Maria Gutierrez-Martinez

    (Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain)

Abstract

Street lighting installations are an essential service for modern life due to their capability of creating a welcoming feeling at nighttime. Nevertheless, several studies have highlighted that it is possible to improve the quality of the light significantly improving the uniformity of the illuminance. The main difficulty arises when trying to improve some of the installation’s characteristics based only on statistical analysis of the light distribution. This paper presents a new algorithm that is able to obtain the overall illuminance uniformity in order to improve this sort of installations. To develop this algorithm it was necessary to perform a detailed study of all the elements which are part of street lighting installations. Because classification is one of the most important tasks in the application areas of artificial neural networks, we compared the performances of six types of training algorithms in a feed forward neural network for analyzing the overall uniformity in outdoor lighting systems. We found that the best algorithm that minimizes the error is “Levenberg-Marquardt back-propagation”, which approximates the desired output of the training pattern. By means of this kind of algorithm, it is possible to help to lighting professionals optimize the quality of street lighting installations.

Suggested Citation

  • Antonio Del Corte-Valiente & José Luis Castillo-Sequera & Ana Castillo-Martinez & José Manuel Gómez-Pulido & Jose-Maria Gutierrez-Martinez, 2017. "An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems," Energies, MDPI, vol. 10(2), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:175-:d:89379
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    References listed on IDEAS

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    1. Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
    2. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
    3. Zaiyong Tang & Paul A. Fishwick, 1993. "Feedforward Neural Nets as Models for Time Series Forecasting," INFORMS Journal on Computing, INFORMS, vol. 5(4), pages 374-385, November.
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    Cited by:

    1. Ana Ogando-Martínez & Javier López-Gómez & Lara Febrero-Garrido, 2018. "Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques," Energies, MDPI, vol. 11(8), pages 1-13, August.
    2. Jihoon Moon & Sungwoo Park & Seungmin Rho & Eenjun Hwang, 2019. "A comparative analysis of artificial neural network architectures for building energy consumption forecasting," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.

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